Spanish News Classification Headlines
SNCH: this model was develop by M47Labs the goal is text classification, the base model use was BETO, it was fine-tuned on 1000 example dataset.
Dataset Sample
Dataset size : 1000
Columns: idTask,task content 1,idTag,tag.
| idTask | task content 1 | idTag | tag |
|---|---|---|---|
| 3637d9ac-119c-4a8f-899c-339cf5b42ae0 | Alcalá de Guadaíra celebra la IV Semana de la Diversidad Sexual con acciones de sensibilización | 81b36360-6cbf-4ffa-b558-9ef95c136714 | sociedad |
| d56bab52-0029-45dd-ad90-5c17d4ed4c88 | El Archipiélago Chinijo Graciplus se impone en el Trofeo Centro Comercial Rubicón | ed198b6d-a5b9-4557-91ff-c0be51707dec | deportes |
| dec70bc5-4932-4fa2-aeac-31a52377be02 | Un total de 39 personas padecen ELA actualmente en la provincia | 81b36360-6cbf-4ffa-b558-9ef95c136714 | sociedad |
| fb396ba9-fbf1-4495-84d9-5314eb731405 | Eurocopa 2021 : Italia vence a Gales y pasa a octavos con su candidatura reforzada | ed198b6d-a5b9-4557-91ff-c0be51707dec | deportes |
| bc5a36ca-4e0a-422e-9167-766b41008c01 | Resolución de 10 de junio de 2021, del Ayuntamiento de Tarazona de La Mancha (Albacete), referente a la convocatoria para proveer una plaza. | 81b36360-6cbf-4ffa-b558-9ef95c136714 | sociedad |
| a87f8703-ce34-47a5-9c1b-e992c7fe60f6 | El primer ministro sueco pierde una moción de censura | 209ae89e-55b4-41fd-aac0-5400feab479e | politica |
| d80bdaad-0ad5-43a0-850e-c473fd612526 | El dólar se dispara tras la reunión de la Fed | 11925830-148e-4890-a2bc-da9dc059dc17 | economia |
Labels:
ciencia_tecnologia
clickbait
cultura
deportes
economia
educacion
medio_ambiente
opinion
politica
sociedad
Example of Use
Pipeline
import torch
from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline
review_text = 'los vehiculos que esten esperando pasajaeros deberan estar apagados para reducir emisiones'
path = "M47Labs/spanish_news_classification_headlines"
tokenizer = AutoTokenizer.from_pretrained(path)
model = BertForSequenceClassification.from_pretrained(path)
nlp = TextClassificationPipeline(task = "text-classification",
model = model,
tokenizer = tokenizer)
print(nlp(review_text))
[{'label': 'medio_ambiente', 'score': 0.5648820996284485}]
Pytorch
import torch
from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline
from numpy import np
model_name = 'M47Labs/spanish_news_classification_headlines'
MAX_LEN = 32
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
texto = "las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno"
encoded_review = tokenizer.encode_plus(
texto,
max_length=MAX_LEN,
add_special_tokens=True,
#return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt',
)
input_ids = encoded_review['input_ids']
attention_mask = encoded_review['attention_mask']
output = model(input_ids, attention_mask)
_, prediction = torch.max(output['logits'], dim=1)
print(f'Review text: {texto}')
print(f'Sentiment : {model.config.id2label[prediction.detach().cpu().numpy()[0]]}')
Sentiment : medio_ambiente```
A more in depth example on how to use the model can be found in this colab notebook: https://colab.research.google.com/drive/1XsKea6oMyEckye2FePW_XN7Rf8v41Cw_?usp=sharing
## Finetune Hyperparameters
* MAX_LEN = 32
* TRAIN_BATCH_SIZE = 8
* VALID_BATCH_SIZE = 4
* EPOCHS = 5
* LEARNING_RATE = 1e-05
## Train Results
|n_example|epoch|loss|acc|
|------|------|------|------|
|100|0|2.286327266693115|12.5|
|100|1|2.018876111507416|40.0|
|100|2|1.8016730904579163|43.75|
|100|3|1.6121837735176086|46.25|
|100|4|1.41565443277359|68.75|
|n_example|epoch|loss|acc|
|------|------|------|------|
|500|0|2.0770938420295715|24.5|
|500|1|1.6953029704093934|50.25|
|500|2|1.258900796175003|64.25|
|500|3|0.8342628020048142|78.25|
|500|4|0.5135736921429634|90.25|
|n_example|epoch|loss|acc|
|------|------|------|------|
|1000|0|1.916002897115854|36.1997226074896|
|1000|1|1.2941598492664295|62.2746185852982|
|1000|2|0.8201534710415117|76.97642163661581|
|1000|3|0.524806430051615|86.9625520110957|
|1000|4|0.30662027455784463|92.64909847434119|
## Validation Results
* n_examples : 100
* Accuracy Score = 0.35
* Precision (Macro) = 0.35
* Recall (Macro) = 0.16228956228956226
* n_examples : 500
* Accuracy Score = 0.62
* Precision (Macro) = 0.6064014202172097
* Recall (Macro) = 0.46789055973266497
* n_examples : 1000
* Accuracy Score = 0.6777777777777778
* Precision(Micro) = 0.6777777777777778
* Recall (Macro) = 0.6409636098344376
